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Results<br />

The key to this section is using the correct statistical test to analyse your data. This is essential and can make and<br />

break the whole of your report if incorrect tests or insufficient details are present.<br />

The important part is to figure out the following:<br />

1. Type of data - nominal, ordinal or interval.<br />

2. Type of Experimental Design/Independent or related items.<br />

3. Is it a test of difference, association or correlation?<br />

There are five common non-parametric statistical tests (which don’t follow strict criterion relating to things such as<br />

the mean and standard deviation that are used:<br />

• Mann Whitney U test.<br />

• Wilcoxon Signed Rank tests.<br />

• Chi Square.<br />

• Binomial Sign test.<br />

• Spearman’s Rho.<br />

Inferential Statistics<br />

When analysing your data, you need to justify why you have used the statistical test in question for data analysis.<br />

Furthermore, you need to include the level of significance. The most common level in psychology is 0.05. This<br />

indicates the results obtained in your study are because of the 95% chance of manipulation of your independent<br />

variable, the rest 5% is due to other factors. When your results are significant, this indicates that you as a researcher<br />

can accept your alternative hypothesis and reject your null hypothesis. You would need to explicitly indicate this.<br />

In addition, all calculations of your statistical test analysis would not appear in this section. Where do you think<br />

they should go?<br />

Activity – On the basis of your aim and hypothesis, experimental design and type of data – which statistical test<br />

would you conduct for your data?<br />

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